Application of Neural Networks for 24-Hour-Ahead Load Forecasting
One of the most important requirements for the
operation and planning activities of an electrical utility is the
prediction of load for the next hour to several days out, known as
short term load forecasting. This paper presents the development of
an artificial neural network based short-term load forecasting model.
The model can forecast daily load profiles with a load time of one
day for next 24 hours. In this method can divide days of year with
using average temperature. Groups make according linearity rate of
curve. Ultimate forecast for each group obtain with considering
weekday and weekend. This paper investigates effects of temperature
and humidity on consuming curve. For forecasting load curve of
holidays at first forecast pick and valley and then the neural network
forecast is re-shaped with the new data. The ANN-based load models
are trained using hourly historical. Load data and daily historical
max/min temperature and humidity data. The results of testing the
system on data from Yazd utility are reported.
[1] D. C. Park, M. A. El-Sharkawi, R. J. MarksII, L. E. Atlas, M. J.
Damborg, "Electrical Load Forecasting Using an Artificial Neural
Network," IEEE Trans. on Power Systems., vol. 6, No. 2, Nov. 1991, pp.
442-448.
[2] T. M. Peng, N. F. Hubele, G. G. Karady, "Conceptual Approach to the
Application of Neural Network for Short-Term Load Forecasting," IEEE
International Symposium on Circuits and Systems., vol. 4, 1990, pp.
2942-2945.
[3] S. T. Chen, D. C. Yu, A. R. Moghaddamjo, "Weather Sensitive Short-
Term Load Forecasting Using Nonfully Connected Artificial Neural
Network," IEEE Trans. on Power Systems., vol. 7, No. 3, Aug. 1992, pp.
1098-1105.
[4] T. S. Dillon, S. Sestito, S. Leung, "An Adaptive Neural Network
Approach in A Power System," IEEE Proc. of 1991 ANNPS., July. 1991,
pp. 17-21.
[5] K. Y. Lee, Y. T. Cha, C. C. Ku, "A Study on Neural Networks for Short-
Term Load Forecasting," Proc. of Applications of Neural Networks to
Power Systems 1991, Seattle, WA, July. 1991, pp. 26-30.
[6] A. Khotanzad, M. H. Davis, A. Abaye, D. J. Martukulam, "An Artificial
Neural Network Hourly Temperature Forecaster with Applications in
Load Forecasting," IEEE Trans. PWRS, vol. 11, No. 2, May. 1996, pp.
870-876.
[7] T. Matsumoto, S. Kitamara, Y. Ueki, T. Matsui, "Short Term Load
Forecasting by Artificial Neural Networks Using Individual and
Collective Data of Preceding Years," Neural Networks to Power
Systems, 1993, pp. 245-250.
[1] D. C. Park, M. A. El-Sharkawi, R. J. MarksII, L. E. Atlas, M. J.
Damborg, "Electrical Load Forecasting Using an Artificial Neural
Network," IEEE Trans. on Power Systems., vol. 6, No. 2, Nov. 1991, pp.
442-448.
[2] T. M. Peng, N. F. Hubele, G. G. Karady, "Conceptual Approach to the
Application of Neural Network for Short-Term Load Forecasting," IEEE
International Symposium on Circuits and Systems., vol. 4, 1990, pp.
2942-2945.
[3] S. T. Chen, D. C. Yu, A. R. Moghaddamjo, "Weather Sensitive Short-
Term Load Forecasting Using Nonfully Connected Artificial Neural
Network," IEEE Trans. on Power Systems., vol. 7, No. 3, Aug. 1992, pp.
1098-1105.
[4] T. S. Dillon, S. Sestito, S. Leung, "An Adaptive Neural Network
Approach in A Power System," IEEE Proc. of 1991 ANNPS., July. 1991,
pp. 17-21.
[5] K. Y. Lee, Y. T. Cha, C. C. Ku, "A Study on Neural Networks for Short-
Term Load Forecasting," Proc. of Applications of Neural Networks to
Power Systems 1991, Seattle, WA, July. 1991, pp. 26-30.
[6] A. Khotanzad, M. H. Davis, A. Abaye, D. J. Martukulam, "An Artificial
Neural Network Hourly Temperature Forecaster with Applications in
Load Forecasting," IEEE Trans. PWRS, vol. 11, No. 2, May. 1996, pp.
870-876.
[7] T. Matsumoto, S. Kitamara, Y. Ueki, T. Matsui, "Short Term Load
Forecasting by Artificial Neural Networks Using Individual and
Collective Data of Preceding Years," Neural Networks to Power
Systems, 1993, pp. 245-250.
@article{"International Journal of Electrical, Electronic and Communication Sciences:57530", author = "Fatemeh Mosalman Yazdi", title = "Application of Neural Networks for 24-Hour-Ahead Load Forecasting", abstract = "One of the most important requirements for the
operation and planning activities of an electrical utility is the
prediction of load for the next hour to several days out, known as
short term load forecasting. This paper presents the development of
an artificial neural network based short-term load forecasting model.
The model can forecast daily load profiles with a load time of one
day for next 24 hours. In this method can divide days of year with
using average temperature. Groups make according linearity rate of
curve. Ultimate forecast for each group obtain with considering
weekday and weekend. This paper investigates effects of temperature
and humidity on consuming curve. For forecasting load curve of
holidays at first forecast pick and valley and then the neural network
forecast is re-shaped with the new data. The ANN-based load models
are trained using hourly historical. Load data and daily historical
max/min temperature and humidity data. The results of testing the
system on data from Yazd utility are reported.", keywords = "Artificial neural network, Holiday forecasting, pickand valley load forecasting, Short-term load-forecasting.", volume = "3", number = "2", pages = "274-4", }